{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ConstantPrBoundary.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMUsDSLyYw8so3hc/ihfB06",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Divyanshu-ISM/Oil-and-Gas-data-analysis/blob/master/ConstantPrBoundary.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "abk-X4HjJDf8",
        "colab_type": "text"
      },
      "source": [
        "#Reservoir Simulation : Constant Pressure Boundary case. \n",
        "\n",
        "Divyanshu Vyas | dvyas13ad@gmail.com\n",
        "\n",
        "Ref. : T. Ertekin | Abou Kassem etc."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nmqD_s0cIhjU",
        "colab_type": "text"
      },
      "source": [
        "![NFB.JPG]()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YCNVJLrnIVjt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l_8N_NjRJ7Kl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "#Initialization\n",
        "\n",
        "# Pi = 6000\n",
        "\n",
        "dx = 1000\n",
        "dy = 1000\n",
        "dz = 75\n",
        "\n",
        "B = 1 #RB/STB\n",
        "c = 3.5*(10**(-6)) #psi-1\n",
        "\n",
        "kx = 15 #mD\n",
        "phi= 0.18\n",
        "\n",
        "mu = 10 #cp\n",
        "\n",
        "dt = 10 #days\n",
        "\n",
        "#Well Block index wb\n",
        "\n",
        "wb = 3 # 0,1,2,3 (block 4)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yhKyw68SKPiL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Calclulation\n",
        "\n",
        "Ax = dy*dz #ft2\n",
        "\n",
        "Vb = dx*dy*dz #ft3\n",
        "\n",
        "qsc_wb = -150 #stb/d\n",
        "\n",
        "Tx = (0.001127)*(kx)*(Ax)/(mu*B*dx)\n",
        "\n",
        "M = 5.615*B*dt/Vb/phi/c"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hi33BfgULhdE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "P = np.ones(5)*6000"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vJD7lOazKUPl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c0d00aff-a152-4fc5-f919-dcc866d923d4"
      },
      "source": [
        "t = np.arange(0,370,dt)\n",
        "# t\n",
        "len(t)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "37"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XLBVZPXIKaBb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#creating a space-time 2D matrix\n",
        "P_r = np.zeros((37,5))"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C6U9wmUeKpXi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "P_r[0] = np.ones(5)*6000\n",
        "\n",
        "# P_r"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f-rVvM0MKtT9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "wb = 3 #can ask user input as well.\n",
        "qsc = np.array([0,0,0,-150,0])\n",
        "\n",
        "#West Boundary Pressure\n",
        "P_w = 6000 #psi\n",
        "\n",
        "for j in range(1,len(t)):\n",
        "\n",
        "  for i in range(1, len(P)-1):\n",
        "\n",
        "    P_r[j,i] = P_r[j-1,i] + M*qsc[i] + M*(Tx*(P_r[j-1,i-1] - P_r[j-1,i]) + Tx*(P_r[j-1,i+1] - P_r[j-1,i]))\n",
        "  \n",
        "  P_r[j,0] = P_w\n",
        "\n",
        "  P_r[j,-1]= P_r[j-1,-1] +  M*qsc[-1] + M*(Tx*(P_r[j-1,-2] - P_r[j-1,-1]))\n"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fRvibw2DMkFe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "P_response = pd.DataFrame(P_r)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "okc5L5C4MnKg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "temp = P_response.copy()\n",
        "\n",
        "temp['Time(days)'] = t"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pxq-U-Y4bVwA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "temp.columns = [0, 1, 2, 3, 4, 'Time(days)']"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BsuGGBKvV4pt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "P_nfb = np.zeros((37,5))\n",
        "P_nfb[0] = P\n",
        "\n",
        "# P_nfb\n",
        "\n",
        "\n",
        "\n",
        "wb = 3 #can ask user input as well.\n",
        "qsc = np.array([0,0,0,-150,0])\n",
        "# T = np.array([0,Tx,Tx,Tx,Tx,0])\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "for j in range(1,len(t)):\n",
        "  \n",
        "  # P[0] = P[0] + M*qsc[0] + M*(Tx*(P[1] - P[0]))\n",
        "  # P[-1] = P[-1] + M*qsc[-1] + M*(Tx*(P[-2] - P[-1]))\n",
        "\n",
        "  for i in range(1, len(P)-1):\n",
        "    P_nfb[j,i] = P_nfb[j-1,i] + M*qsc[i] + M*(Tx*(P_nfb[j-1,i-1] - P_nfb[j-1,i]) + Tx*(P_nfb[j-1,i+1] - P_nfb[j-1,i]))\n",
        "  \n",
        "  \n",
        "  P_nfb[j,0] = P_nfb[j-1,0] +  M*qsc[0] + M*(Tx*(P_nfb[j-1,1] - P_nfb[j-1,0]))\n",
        "  P_nfb[j,-1]= P_nfb[j-1,-1] +  M*qsc[-1] + M*(Tx*(P_nfb[j-1,-2] - P_nfb[j-1,-1]))"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dISTqSoiWnW-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_nfb = pd.DataFrame(P_nfb)\n",
        "\n",
        "temp_nfb = df_nfb.copy()\n",
        "\n",
        "temp_nfb['Time(days)'] = t"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DoxAwOY8X8bM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# temp_nfb\n",
        "# temp_nfb.columns = [ '0' , '1' , '2' , '3' , '4' , 'Time(days)']"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bBLp8VsuSaVK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "wb = 3 #can ask user input as well.\n",
        "qsc = np.array([0,0,0,-150,0])\n",
        "\n",
        "#West Boundary Pressure\n",
        "# P_w = 6000 #psi\n",
        "\n",
        "for j in range(1,len(t)):\n",
        "\n",
        "  for i in range(1, len(P)-1):\n",
        "\n",
        "    P_r[j,i] = P_r[j-1,i] + M*qsc[i] + M*(Tx*(P_r[j-1,i-1] - P_r[j-1,i]) + Tx*(P_r[j-1,i+1] - P_r[j-1,i]))\n",
        "  \n",
        "  P_r[j,0] = P_w\n",
        "\n",
        "  P_r[j,-1]= P_r[j-1,-1] +  M*qsc[-1] + M*(Tx*(P_r[j-1,-2] - P_r[j-1,-1]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "urpWf4cRbQbq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PgIIkSesMpQc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#A very nice Realization. \n",
        "\n",
        "#Imagine a Constant Pressure boundary at the left to be like a STRONG AQUIFER.\n",
        "\n",
        "#1.\n",
        "#Observe that to make sure the boundary Pressure is constant, the adjacent block\n",
        "# ie, block 1 must have P1 = P_w = 6000, so that no pressure drop and hence no flow\n",
        "# and constant pr. throughout. \n",
        "\n",
        "#2. \n",
        "#A constant Pressure Strong Aquifer right next to a block (ideally) would never \n",
        "#let the block pressure drop. Right? Yes. That's why P = 6000 Throughout. "
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6mgZse4Jarjj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "8f9c00c1-94cf-424c-c87b-c3293e852fc8"
      },
      "source": [
        "temp_nfb.columns = ['0' , '1' , '2' , '3' , '4' , 'Time(days)']\n",
        "\n",
        "temp_nfb.columns"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['0', '1', '2', '3', '4', 'Time(days)'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EoiQIlJ_QNh3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "581e7ff1-bf0f-4093-fa3b-c264b6c0dfb7"
      },
      "source": [
        "plt.figure(figsize=(12,4))\n",
        "\n",
        "plt.style.use('default')\n",
        "\n",
        "plt.plot(temp['Time(days)'] , temp[3] ,marker = 'o', label='Const. Pr. B. (Strong Aquifer),#3(Well-block)')\n",
        "plt.plot(temp_nfb['Time(days)'] , temp_nfb['3'],marker = 'o', label='No Flow Boundary,#3(Well-Block)')\n",
        "\n",
        "plt.plot(temp['Time(days)'] , temp[1] ,marker = 'D', label='Const. Pr. B. (Strong Aquifer),Block#1')\n",
        "plt.plot(temp_nfb['Time(days)'] , temp_nfb['1'],marker = 'D', label='No Flow Boundary, Block#1')\n",
        "\n",
        "plt.ylabel('Reservoir Pressure(Psia)')\n",
        "plt.xlabel('Time(days)')\n",
        "\n",
        "plt.title('Simulating Aquifer Effects')\n",
        "\n",
        "plt.grid()\n",
        "\n",
        "plt.legend(loc='best')"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f157d4109b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1200x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LCDtTQ0mR1Ur",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "488968b1-02c8-4d0d-f7e6-4a237644d045"
      },
      "source": [
        "# plt.style.use('default')\n",
        "\n",
        "# temp_nfb[0]\n",
        "\n",
        "temp.head()"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>Time(days)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6000.0</td>\n",
              "      <td>6000.000000</td>\n",
              "      <td>6000.000000</td>\n",
              "      <td>6000.000000</td>\n",
              "      <td>6000.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6000.0</td>\n",
              "      <td>6000.000000</td>\n",
              "      <td>6000.000000</td>\n",
              "      <td>5821.746032</td>\n",
              "      <td>6000.000000</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6000.0</td>\n",
              "      <td>6000.000000</td>\n",
              "      <td>5973.142623</td>\n",
              "      <td>5697.206817</td>\n",
              "      <td>5973.142623</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6000.0</td>\n",
              "      <td>5995.953421</td>\n",
              "      <td>5935.614184</td>\n",
              "      <td>5602.102885</td>\n",
              "      <td>5931.567605</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6000.0</td>\n",
              "      <td>5987.471853</td>\n",
              "      <td>5894.455577</td>\n",
              "      <td>5523.738961</td>\n",
              "      <td>5881.927430</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        0            1            2            3            4  Time(days)\n",
              "0  6000.0  6000.000000  6000.000000  6000.000000  6000.000000           0\n",
              "1  6000.0  6000.000000  6000.000000  5821.746032  6000.000000          10\n",
              "2  6000.0  6000.000000  5973.142623  5697.206817  5973.142623          20\n",
              "3  6000.0  5995.953421  5935.614184  5602.102885  5931.567605          30\n",
              "4  6000.0  5987.471853  5894.455577  5523.738961  5881.927430          40"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BT5aVvs4b8A-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}